Local best Artificial Bee Colony algorithm with dynamic sub-populations

The Artificial Bee Colony (ABC) algorithm is a powerful continuous optimization tool that has been proposed in the past few years. Many studies have shown the superior performance of ABC when compared to other well-known optimization algorithms. In this paper, the implementation of an ABC algorithm with dynamic sub-populations (ABCDP) is presented. The algorithm is compared against a number of previously proposed ABC algorithms guided by global-best information. The comparison is based on the final solution reached, robustness, and number of successfully solved functions for all the algorithms when applied to the well-known CEC05 benchmark functions.